DBNscore

This function calculates the score of a DBN defined by its compact adjacency matrix. 
Acceptable data matrices are homogeneous with all variables of the same type: continuous,
binary or categorical. The BGe score is evaluated in the case of continuous data and the BDe score is evaluated for binary and categorical variables.

Implementation of a collection of MCMC methods for Bayesian structure learning
of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient
inference on larger DAGs, the space of DAGs is pruned according to the data. To filter
the search space, the algorithm employs a hybrid approach, combining constraint-based
learning with search and score. A reduced search space is initially defined on the basis
of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with
search and score. Search and score is then performed following two approaches:
Order MCMC, or Partition MCMC.
The BGe score is implemented for continuous data and the BDe score is implemented
for binary data or categorical data. The algorithms may provide the maximum a posteriori
(MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data.
All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks.
References:
J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>,
N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>,
J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>,
M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>,
D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>,
P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>.

Polina Suter

BiDAG

Bayesian Inference for Directed Acyclic Graphs

DBNscore function

<dl><dt>scorepar</dt>
<dd>an object of class <code>scoreparameters</code>, containing the data and scoring parameters; see constructor function <code>scoreparameters</code></dd>
<dt>incidence</dt>
<dd>a square matrix, representing initial and transitional structure of a DBN; the size of matrix is 2*nsmall+bgn, where nsmall is the number of variables per time slice excluding static nodes and bgn is the number of static variables
 the matrix entries are in <code>{0,1}</code> such that <code>incidence[i,j]</code> equals
 1 if there is a directed edge from node <code>i</code> to node <code>j</code> in the DAG and 
<code>incidence[i,j]</code> equals 0 otherwise</dd></dl>

Arguments

Author

Calculating the BGe/BDe score of a single DBN — DBNscore

<dl>

<dt>scorepar</dt>
<dd>an object of class <code>scoreparameters</code>, containing the data and scoring parameters; see constructor function <code>scoreparameters</code></dd>


<dt>incidence</dt>
<dd>a square matrix, representing initial and transitional structure of a DBN; the size of matrix is 2*nsmall+bgn, where nsmall is the number of variables per time slice excluding static nodes and bgn is the number of static variables
 the matrix entries are in <code>{0,1}</code> such that <code>incidence[i,j]</code> equals
 1 if there is a directed edge from node <code>i</code> to node <code>j</code> in the DAG and 
<code>incidence[i,j]</code> equals 0 otherwise</dd>

</dl>

DBNscore: Calculating the BGe/BDe score of a single DBN

Description

Usage

Value

Arguments

Author

Examples